All the models involved are learnt automatically from data, so the system is very flexible with changes in the reference language, changes in POS tags or changes in the definition of chunks.

this sequence must be compatible with the contextual, syntactical and lexical constraints.


The training sets are very small, however, so that the need for feature restrictions disappears and the full model can be used.

4) In the R&M WPDV model, Ramshaw and Marcus's type of IOB-tags are used, i.e.


As expected this variant gave slightly better results.

we have limited our search to single classifiers, thereby explicitly ignoring the possibility to build a metalearning classifier architecture that could be expected to improve accuracy.


In the absence of information about feature relevance, this is a reasonable choice.

the results are comparable to the best tiMBL settings.


But the rudimentary way in which we implemented the approach is likely also to blame.

we would expect to obtain better results if we did not obliterate the part-of-speech of a lexeme in the process of tagging it with a chunk label.


In this paper we will first briefly introduce substructural logic in section 2.

concept ff then describes a subset of concept f because on every position in the cNF formula contains a subset of the allowed variables; i.e.


Note that to induce a rule we only need one sentence where the rule is needed oncc our assumption is that in real (large) training datasets there will be enough sentences for this to be true for any missing grammar rule.

we prefer to have the user only perform the latter task, but the advantage of wirth's approach is that the user can constrain the search at an earlier stage.


Two drawbacks of this approach can be observed.

we are able to detect such cases and recognize the "pure" verb groups afterwards.


2. turn-to (Semcor 1, class-to-class 2): the relations "take_up-obj-position" and "call_onobj-esprit_de_corps" introduce noise and point to the class <artifact, artefact>.

word-to-word gets the highest precision of all three, but it can only be applied on a few instances.


The class2class model showed a better behavior with the examples in which church appeared as subject.


All the classes allow the same subcategorizations transitive and intransitive, entailing that they cannot be discriminated by subcategorization alone.

we propose the use of multilingual corpora in the automatic classi cation of verbs.


The results obtained in previous works relying on semantic classes are above ours (around 0.82 for (Brill and Resnik, 1994) and 0.77 for (Lauer and Dras, 1994)), but a direct comparison is difficult inasmuch as only three-word sequences (V N P, for (Brill and Resnik, 1994) and N N N for (Lauer and Dras, 1994)) were used for evaluation in those works, and the language studied is English.

due to the possible ambiguities the French language displays (e.g.


Since the classifiers of the combination scheme have a tendency of being naturally biased towards classifying the examples as negative, we assume that a classifier making too many positive decision is probably doing so unreliably.

15 Finally, the data set was divided into 10 concept learning problems where each problem consisted of a positive class containing 100 examples sampled from a single top 10 Reuters topic class and a negative class containing the union of all the examples contained in the other 9 top 10 Reuters classes.


Such a problem can easily be tackled using resampling methods.

the classifier selected for a single data point needs not be the same as the one selected for a different data point.


This paper presents a method for combining different expressions of the re-sampling approach in a mixture of experts framework.

any classifier that classifies more unlabeled data points as positive than the threshold established for the expert to which this classifier belongs needs to be discarded.


We have experienced that for different reasons some systems perform worse than others and including their results in the majority vote decreases the combined performance.

we have applied majority voting and nine other combination methods to the output of the learning systems that were applied to the three tasks.


We can represent this as is the rule, and is the index saying where in the right hand side it occurs.

this means that optimising the likelihood of the model may not lead you to a linguistically plausible grammar.


There is therefore no relation between what happens immediatly before it, and what happens immediately after it.

following this, we aggregate rules with the same right hand sides and repeat the operation.


It is not always clear whether these analyses should be considered mistakes.

second, and more importantly, in an ML approach, there is pressure for the symbols and rules in a PCFG to align in ways which maximize the truth of the conditional independence assumptions embodied by that PCFG.


This dependence on the tagset for constituent identification is very undesirable.

none of these arguments have anything to do with conditional independence; traditional linguistic con-stituency reflects only grammatical possibilty of expansion.


In the case of nouns and verbs, which are the concern of the present paper, the unique beginners are the most general concepts under which the entire set of entries is organized on the basis of hyponymy/hypernymy relations.

in the remaining cases the chi-square test is applied.


As previously mentioned, the unique beginners are too general to be satisfactorily classified as animate or inanimate.

the system was evaluated with respect to two corpora.


Consider the following list of simple NPs selected as candidates: 1. conference workshop announcement 2. international conference 3. workshop description 4. conference deadline In the case of the first noun phrase, for example, its importance is found in the two noun modifiers: conference and workshop as much as in the head announcement, due to their presence as heads or modifiers in the candidate NPs 2-4.

the axis-parallel decision trees check at each node the value of a single attribute.


np length words: Noun phrase length measured in number of words, normalized by dividing it with the total number of words in the candidate NP list.

for some specific tasks in NLP , the head is not necessarily the most semantically important part of the noun phrase.


Psychology and psycholinguistics provide us with a significant amount of data from which we may derive a fairly good picture of how the problem is defined for humans.

in terms of a learning system this would suggest the availability of some initial lexical information like word groupings or some bootstrapping lexicon.


Over the WSJ, the Brill tagger achieves a modest tag recall of 0.103 for particles, and tag precision of 0.838.

as a research testbed and target resource to expand/domain-tune, we use the LinGO English Resource Grammar (LinGO-ERG), a linguistically-precise HPSG-based grammar under development at CSLI (Copestake and Flickinger, 2000;Flickinger, 2000).


it is highly conservative in allocating particle tags, to the extent that it recognises only two particle types for the whole of the WSJ: out and down.


One example where these assumptions do not hold is for the Romanian word a, which has 5 possible POS tags, including Infinitive_Marker (corresponding to the English word to).

to test the relative contributions of limited native speaker help when available, for roughly 4 additional total person hours in a second test condition for Romanian a native speaker corrected and augmented gaps in the patterns previously entered from the grammar book, focusing almost exclusively on the complex inflections of closed-class words.


Banko and Brill (2001) report on confusion set disambiguation experiments where they apply relatively simple learning methods to a one billion word training corpus.

having access to very large corpora ought to reduce the need for smoothing, and so ought to allow us to design simpler systems.


Therefore, we define a biased probability distribution, p c (y), to be used by the CD clustering algorithm for characterizing a cluster c. It is designed to call attention to y's that are typical for cluster members in all, or most, different datasets.

the common set of features consists of all corpus words that occur in at least three different documents within two or three of the corpora, excluding a list of common function words.


In addition to our tags -e 1 …e 8 -the expert gave a title to each cluster.

composite and rare terms as well as phrases that the software has inappropriately segmented were filtered out.


GraSp is designed specifically for inducing grammars from large, unlabelled corpora of spontaneous (i.e.

5 The current GraSp implementation completes a learning session in about one hour when fed with our main corpus.


Of course such experiments are side-issues.

presented with similar streams of syllables, the GraSp learner too discovers word-hood.


as discussed in the next section, learning from non-verbal sources may serve as an inspiration in the L1 learning domain also.


Finding the optimal tag sequence for a given sequence of words can be done in the usual fashion using Viterbi decoding.

we only consider first order models in the rest of this paper.


In this paper, we propose a new statistical Japanese dependency parser using a cascaded chunking model.

their model can take longer context into account, and disambiguate complex dependency relations.


In this paper, we consider a number of algorithms for estimating the parameters of ME models, including Generalized Iterative Scaling and Improved Iterative Scaling, as well as general purpose optimization techniques such as gradient ascent, conjugate gradient, and variable metric methods.

for the comparison, we implemented both Generalized and Improved Iterative Scaling in C++ using the primitives provided by PETSc.


This suggests that more comprehensive experiments to compare the convergence rate and accuracy of various algorithms on a wider range of problems is called for.

even modest Me models can require considerable computational resources and very large quantities of annotated training data in order to accurately estimate the model's parameters.


Since the log-likelihood function is concave, the method of steepest ascent is guaranteed to find the global maximum.

we can adapt GIS to estimate the model parameters θ rather than the model probabilities q, yielding the update rule: The step size, and thus the rate of convergence, depends on the constant C: the larger the value of C, the smaller the step size.


Thanks also to Stephen Clark, Andreas Eisele, Detlef Prescher, Miles Osborne, and Gertjan van Noord for helpful comments and test data.

highly efficient, accurate, scalable methods are required for estimating the parameters of practical models.


While this kind of HMM is simple and easy to construct and apply, it has its limitations.

perhaps the discrepancy (especially for the F l 8 9 G 6 E class) reflects differences in the way the training data was annotated; F l 8 9 G 6 E is a highly heterogenous class, and the criteria for distinguishing between  The models described here are very simple and efficient, depend on no preprocessing or (with the exception of s w v ¦ ) external databases, and yet provide a dramatic improvement over a baseline model.


Sooner or later, the total number of unseen entities decreases.

despite of the good results when using inferred rules as described above for our algorithm, we hope to improve the method as a whole with respect to the size of the input knowledge.


On the other hand, there are elements that often appear in named entities, but are not characteristic for them (like the first name Israel).

further, recall will rise with a growing knowledge size.


Errors occur in the case of words, which are mainly used in positions which are often occupied by first names.

all children of Theo will be first names; hence they get the feature firstname.


Each of these techniques were shown to be useful (Spanish test set: F =1 =75.78 Dutch test set: F =1 =70.67).

incorporating a list of person names in the training process did not help (Spanish test set: F =1 =73.66 Dutch test set: F =1 =68.08) Jansche (2002) employed a rst-order Markov model as a named entity recognizer.


In short, the probability of the complete structure is defined as the product of probabilities of lexical dependencies.

this model has a crucial flaw: the maximum likelihood estimation of semantics probability is intractable.


It takes about 2 days to train a slice on a P3 1.13GHz processor.

if the output set is large enough, the reranking approach may help to alleviate the impact of the label bias problem, because the victim parses (i.e.


≤40 Words (2245 sentences where f i is the result of the ith SVM.

slice i contains positive samples ((p k , p ki ), +1) and negative samples ((p ki ,p k ), −1), wherep k is the best parse for sentence k, p ki is the parse with the ith highest loglikelihood in all the parses for sentence k and it is not the best parse.


The main difference between Basilisk and Meta-Bootstrapping is that Basilisk scores each noun based on collective information gathered from all patterns that extracted it.

4    Meta-Bootstrapping and Basilisk are semi-automatic lexicon generation tools because, although the bootstrapping process is 100% automatic, the resulting lexicons need to be reviewed by a human.


In this section, we briefly overview these bootstrapping algorithms and explain how we used them to generate lists of subjective nouns.

our work classifies individual sentences, as does the research in (Wiebe et al., 1999).


Selective term weighting has been shown to be highly effective for information retrieval.

information extraction techniques can add high precision, categorical information such as approximate age/date-of-birth, nationality and occupation.


This type of clustering would be useful to someone searching for a specific individual named Jim Clark.

in practice these pseudoname pair situations may be more difficult than expected for naturally occurring name pairs.


In this paper, we refer to the former method as CV-EM and the latter method as CV-EM2.

these numbers show that our method is somewhat superior to CV-EM.


For one word 'mae' in nouns and three words 'kuru ', 'koeru' and 'tukuru' in verbs, CV-EM was superior to CV-EM2.

furthermore, their methods used syntactic analysis, but our methods do not need it.


This means that cross validation is use-less, so it is difficult to estimate an optimum iteration number in the EM algorithm.

in the same way, we get 'e4=suru' and 'e4=.'.


As two independent feature sets, one is the context surrounding the target word and the other is the heuristic of 'one sense per discourse'.

both the left context and right context can judge the meaning of the target word by itself, and are independent.


First, the agreement-based method dynamically selects the best sample at each stage, which may not be the whole cache.

the performance of the bootstrapped taggers is still a long way behind a tagger trained on a large amount of manually annotated data.


The standard evaluation data is the Wall-Street Journal (WSJ) tree-bank.

the above parameter choices may not be optimal for sentence segmentation.


Roughly speaking, the rules will classify more than half of the periods.

in this paper we do not investigate methods that solve these issues.


Next, we showed that some exceptionality measures can be used as means to improve the prediction accuracy on our tasks by combining the prediction of our learners based on measures of instance exceptionality.

the typicality values are interpreted as follows: if the value is higher than 1, then that instance has an intra-concept similarity higher than inter-concept similarity, thus one can say that the instance is a good example of its class (it is a typical instance).


When inter-concept similarity is higher than 0, we will set the local typicality to a minimum value if its intra-concept similarity is 0 (so that we will not have a big gap between local typicality values).

with the previous study, where for all tasks even the smallest editing led to significant accuracy decreases, for our task there was no clear decrease in performance.


We also find that our semi-supervised method (Seed) is linguistically plausible, and performs as well as or better than features manually determined based on linguistic knowledge (Ling).

the semisupervised approach (using a seed set of sample verbs) overall outperforms not only the full set of features, but the hand-selected features as well.


WordNet (Fellbaum, 1998), a manually constructed lexical reference system provides a lexical database along with semantic relations among the lexemes of English and is widely used in NLP tasks today.

it takes a word token and an optional context as input, and returns the sense of the word in that context.


For naturallanguage applications where target classes correspond to words, this produces an economical and powerful representation.

we investigated the relevance of different confidence features by using the IROC values of single-feature models for the 1-4 word prediction task, with both Maxent1 and Bayes base models.


From the brief description above it should become apparent that heavy reliance on feature-tagged dictionaries and/or parsers becomes a new bottleneck for Machine Translation development.

rather, we use this information to classify words from the target language dictionary.


Various morpheme extraction algorithms can be applied to the data.

as mentioned above, we tag nouns, adjectives, and verbs with PLURaL vs. SINGULaR values, and additionally verbs with PaST vs. NON-PaST information.


Partial matches are allowed, so that if the algorithm assigned one correct and one incorrect pos, the correctly assigned label is taken into account in the accuracy measure.

for each French word we only retain the two most likely tags and rescale their probabilities so that they sum to 1.


For example, using the correct seed-pair dill < herb we retrieved the following suggested hyponyms for herb: Semantically Similar Words dill rind fennel seasoning juice sauce pepper parsley vinegar oil pur All of these items are related to dill, but only some of them are herbs.

our initial experiment was to construct a hyponymy extraction system based on the six lexicosyntactic patterns identified in (Hearst, 1998), which are listed in Table 1.


We demonstrate that the method is applicable to more generic situations than those previously reported, where the number of random variables is arbitrary.

our probability model is a class-based model and it is an extension of the model proposed by Li and Abe (1998).


Doing such an exhaustive search is almost impossible for a class of modest size, because the time complexity of such an exhaustive search is of exponential order to the size of the class.

figure 1 shows the computation time as a function of the size of the vocabulary, i.e., the number of nouns plus the number of case frame slots (i.e., pp:pred) in the training data.


In this paper, we basically take a top-down, divisive strategy, but at each stage of division we do CLASSIFY operations on the set of classes at each stage.

to classify elements in each ½ Ò , we classified the elements in each in order.


These four tables correspond to the cases in which the number of labeled examples is 32, 64, 128 and 256 as indicated by the table captions.

the features of a target are the two preceding words and the two following words.


The naive Bayes classifier can be combined with the wellestablished EM algorithm to exploit the unlabeled data .

figure 2 shows the relationship between the classification performance and the amount of unlabeled data for three confusion sets: {I, me}, {principal, principle}, and {passed, past}.


Experimental results are presented in Section 5.

disastrous performance deterioration can be observed, especially that of the basic EM algorithm, in some confusion sets including {among, be-tween}, {country, county}, and {site, cite}.


Empty fillers are not considered similar.

a phenomenon we refer to as self-cooccurrence complicates the augmentation process.


Lin (1998) created a thesaurus using syntactic relationships with other words.

the second, REVISE, removes nodes that do not meet the InSeq criterion when an event acquires a second neighbor through further correlation expansion.


NEC is regarded as a classification task, consisting of assigning the NE type to each already recognized NE.

named Entity classification is viewed as a 4-class classification problem (with LOC, PER, ORG, and MISC class labels), which is straightforwardly addressed by the use of a multiclass learning algorithm.


The next set of contextual predicates encode extra information about NE tags in the current context.

a ME tagger can easily deal with diverse, overlapping features.


The MaxEnt classifier computes the posterior class probability of an example by evaluating the normalized product of the weights active for the particular example.

on the development data, the former estimation type obtains a lower performance than the latter.


To facilitate comparison with other classifiers for this task, most reported results are obtained by using features exclusively extracted from the training data.

this procedure may lead to ties, where some classes receive the same number of votes -one usually resorts to randomly selecting one of the tied candidates in this case - Table 2 presents the average results obtained by this method, together with the variance obtained over 30 trials.


The input gate thus controls the flow of activation into the cell.

the results reported for each network are averaged over 5 runs from different randomised initial weight settings.


Switching to the character model raised the overall score greatly, from 74.5% to 82.2%.

adding even just the previous and next words and tags as (atomic) features raised performance to 82.39%.


5 We present a hill-climb over feature sets for the English development set data in table 2.

repeated sub-elements (usually last names) of multi-word person names were given type PERSON, and a crude heuristic restoration of B-prefixes was performed.


SVM-Lattice: Features 1-10 (listed above in the Features section) 4.

language independence is difficult to achieve in named entity recognition (NER) because different languages appear to require different features.


We are currently building a more sophisticated replacement.

while based on the same exponential form as maximum entropy models, they have efficient procedures for complete, non-greedy finite-state inference and training.


(Thus we transform the gain from a sequence problem to a token classification problem.

(Using a set of fixed conjunction patterns instead of feature induction results in F1 73.34%, with about 1 million features; trialand-error tuning the fixed patterns would likely improve this.)


(2) In many sequence problems, the great majority of the tokens are correctly labeled even in the early stages of training.

(we transform the gain from a sequence problem to a token classification problem.


In this paper, the division and subsequent recombination of subgroups is treated as a meta-learning task.

in order to make classifications, we employ a metalearning strategy that is a variant of stacking (Wolpert, 1992) and cascading (Gama and Brazdil, 2000) over an ensemble of classifiers.


The training data was converted to use the IOB2 phrase model (Tjong Kim Sang and Veenstra, 1999).

rather than explicitly defining B, trie-based probability estimates are used directly within the standard Viterbi algorithm, which exploits dynamic programming to efficiently search the entire space of state assignments.


In European languages, named entities are often distinguished through their use of capitalisation.

since an MD-trie is only built as deep as is required to capture differences between categories, most paths will still be quite shallow.


This model gave the best overall results on the task as a whole.

one of the most straightforward approaches to stacking can be applied to tasks that are naturally divisible into hierarchically ordered subtasks.


The gazetteer supplied for the shared task was found not to improve performance significantly, because our models were already adequately powerful to correctly identify most of the named entities supplied by the gazetteer.

voting was thus used to further improve the base model.


Table 2 shows the result of stacking STBL on the boosting base model.

most current NER systems which have impressive performances have been specially constructed and tuned for English MUC-style documents.


The goal of our learning system is to predict the classlabel value t i associated with each token w i .


Although dictionaries are language dependent, they are often fairly readily available and providing them does not pose a major impediment to customizing a language independent system.

we will consider features listed in Table 1.


The other framework, inference with classifiers (Roth, 2002), views maintaining constraints and learning classifiers as separate processes.

tables 2 & 3 show the performance of each approach in F β=1 using 5-fold cross-validation.


Observing the algorithm performs on other, specific, forced decision tasks verifies that LP is reliable in these situations.

this phenomenon may link to the theory of unimodularity.


In fact, each of the components of our problem here, the separate task of recognizing named entities in sentences and the task of recognizing semantic relations between phrases, can be modeled this way.

for the entity classifier, one set of features are extracted from words within a size 4 window around the target phrase.


Let x {Rij ,r,Ei,e1} be a {0, 1}-variable indicating whether relation R ij is assigned label r and its first argument, E i , is assigned label e 1 .

we use: Similarly, we use d 2 to force the consistency of the second argument of a relation.


Let x {u,i} be a {0, 1}-variable, defined to be 1 if and only if variable u is labeled i, where u ∈ E, i ∈ L E or u ∈ R, i ∈ L R .

it assumes that the entity classifier knows the correct relation labels, and similarly the relation classifier knows the right entity labels as well.


The aim of bootstrapping is to compensate for the paucity of labeled examples.

5 We generate a matrix so that its columns are the ¤ lengthnormalized count vectors.


Let  is a vector of the difference between true (but unknown) feature occurrence probabilities and their maximum likelihood estimations.

our experiments show that Spectral consistently outperforms all the tested methods that employ techniques such as EM and co-training.


We also tested left context vs. right context (not reported in this paper), and found that random split performs slightly better.


Figure 5 shows the curves for the three roles Arg0, Arg1 and ArgM, respectively for linear and semantic kernel whereas Figure  6 shows the multi-class classifier f 1 plots.

in this paper a new kernel for automatic classification of predicate arguments has been designed and experimented.


This problem can be divided in two subtasks: (a) detection of the target argument boundaries, i.e.

if two structures differ only for a few nodes (especially terminal or near terminal nodes) the similarity remains quite high.


In order to accurately capture the amount of redundancy and complementarity among two feature sets, we introduce two measures: the Baseline Ensemble and the Optimal Ensemble.

it may be noted that a break down of accuracies (not shown here for sake of brevity) for noun, verb and adjective target words, of the SENSEVAL-1 and SENSEVAL-2 data revealed that the adjectives were disambiguated best using the Head word and Phrase combination.


However, when used in combination with other features, they may be useful.

(Lin, 1997) takes a supervised approach that is unique as it did not create a classifier for every target word.


We say that these are hypothetical in that they can not be implemented, but rather serve as a post disambiguation analysis technique.

these features are boolean, and indicate if a particular sequence of tags has occurred or not.


In the following, we show that there is a big gap between the performance achieved for some optimal parameter settings, selected through measurements performed on the test data, and the performance level when these parameters are set empirically, suggesting that more research is required to narrow this gap, and make these bootstrapping algorithms useful for practical applications.

for each word, the parameter setting (growth size G / pool size P / iterations I) leading to the highest improvement is determined.


Maximum of M keywords occurring at least N times are determined for each sense of the ambiguous word.

most of the times, it may not be possible to find these optimal parameter values.


As yet there has not been any systematic study to determine which set of techniques results in better sense discrimination.

in similarity space, each instance can be viewed as a node of a fully connected weighted graph whose edges indicate the similarity between the instances they connect.


Bigrams are ordered pairs of words that co-occur within five positions of each other.

finally we present an analysis of our experimental results, and conclude with directions for future work.


At each iteration it selects one cluster whose bisection optimizes the chosen criteria function.

the second order context representation relies on the first order context vectors of feature words.


Then we describe our approach to the evaluation of unsupervised word sense discrimination.

: In vector space, average link starts by assigning each vector to a single cluster.


It is inspired by the nearest neighbor approach in statistical pattern recognition and artificial intelligence (Fix and Hodges, 1952), as well as the analogical modeling approach in linguistics (Skousen, 1989;Skousen, 1992).

the quality of this classifier has been evaluated with respect to both prediction accuracy and parsing accuracy.


For the experiments reported in this paper, we have used the software package TiMBL (Tilburg Memory Based Learner), which provides a variety of metrics, algorithms, and extra functions on top of the classical k nearest neighbor classification kernel, such as value distance metrics and distance weighted class voting (Daelemans et al., 2003).

the Penn Treebank of American English (Marcus et al., 1993) has been used to train and evaluate the best available parsers of unrestricted English text (Collins, 1999;Charniak, 2000).


Another advantage of the kernel method is that although it deals with a high dimensional (possibly infinite) space, explicit computation of high dimensional vectors is not required.

the number of latent classes z k can affect the value of the kernel function.


The TOP kernel consists of features which can minimize D(fθ).

the derivatives other than (38) and (38) directly depend on the distance from a hyperplane, rather than on the value of each feature.


The total number of categories is actually 116.

for that purpose, we first define a function The first-order Taylor expansion of If fθ is of the following form: and if w and b are properly chosen as then (5) is approximately satisfied.


We focus on negative examples in binary classification.

new types of kernels have more recently been proposed; they are based on the probability distribution of examples.


Let È´Ý Üµ be an unknown conditional distribution over inputs, Ü, and output classes, Ý ¾ Ý ½ , Ý ¾ , ¡ ¡ ¡ , Ý Ò , and let È´Üµ be the marginal 'input' distribution.

this is a difficult learning task and generalization to the test set is quite reasonable.


The first two are large-scale generic thesauruses, both constructed using the similarity metric described in (Lin, 1998b), but based on different corpora.

similarity-based techniques do not discard any data.


The first problem is that one needs to generalize the tree cut profiles to their common subsumers to use skew divergence.

in McCarthy (2000), the distributions are propagated to the lowest common subsumers (i.e., the nodes labelled B, C, and D).


an argument boundary inside a chunk is indicated by the part of speech of the last included word: For boost the sequence is VP(NN).


They can describe multi-chunk and partchunk arguments, and by approximating constituents, they allow the use of linguistically informed features.

eM features tend to increase the precision of a model at the cost of recall.


This technique combines classifier wrapping (using the training material internally to test experimental variants) (Kohavi and John, 1997) with progressive sampling of training material (Provost et al., 1999).

we transform the semantic role labeling task to a classification task in which we decide for all pairs of verbs and chunks whether they stand in a semantic role relation.


The POS tag of a preposition is replaced with the preposition itself.

• Predicate POS Context : The POS tags of the words that immediately precede and follow the predicate.


• Distance: we have two notions of distance; the first is the distance of the token from the predicate as a number of base phrases, and the second is the same distance as the number of VP chunks.

the P-by-P semantic role chunker classifies larger units, ignores some of the words, uses a relatively larger context for a given window size and performs the labeling faster.


Path As in (Pradhan et al., 2003), this feature is an ordered list of the chunk types intervening between the target verb and the current word, with consecutive NP chunks treated as one.

finally, we must determine a mapping from our word-by-word tag assignment to the labeled bracketing used to identify semantic arguments in the test data.


For the first module, we use the IOB2 format to determine whether a chunk belongs to an argument or not.

the POS sequence and length features showed no effect, and their omission even slightly improved performance.


where m is the number of constituents covered by the immediate clause, Φ 1 is a feature set for immediate clause, and Φ 2 is a feature set for upper clauses.

we use the features presented in Table 1 for semantic role labeling.


Because we decide the unit of a constituent as a chunk or a subclause, words except the predicate in the target phrase 1 do not belong to constituent.

table 4 also shows the performance of each phase.


No duplicate argument classes for A0-A5,V.

we evaluate the overall performance of our system (without assuming perfect phrases) by training and evaluating the phrase classifier on the output from the first phase (see Table 3).


The following features are used: • Word feature includes the current word, two words before and two words after.

experimental evidence has shown that SNoW activations are monotonic with the confidence in the prediction it can provide a good source of probability estimation.


Term similarities are used to design document similarities which are the core functions of most TC algorithms.

for the SK evaluation we implemented the Eq.


Since it used much more specific labels (the cross-product of the syntactic and semantic labels), its parameter estimates are potentially subject to much greater sparse-data problems.

since N1 is a part-ofspeech (POs) node, its semantic label directly determines its MR, which becomes bowner( ).


The implementation of semantic subcat frames is similar to syntactic subcat frames.

sCIssOR integrates syntactic and semantic processing, allowing each to constrain and inform the other.


If they independently modify the third one (which implies a right bracketing), then we could expect to see also a form with the first two words switched, e.g., if we are given "adult male rat", we would also expect "male adult rat".

we also introduce a new set of surface features for querying web search engines which prove highly effective.


When a contextual token is observed, it is always counted as a single occurrence.

given a corpus, we first derive a list of words occurring with sufficient marginal frequency to support a distributional comparison.


These (De Roeck et al., 2004a;De Roeck et al., 2004b) provide evidence of the fact that the "bag of words" assumption is invalid.

the top part of the table consists of the very frequently occurring function words occurring frequently throughout the corpus.


Earn, Acquisition, Money-fx, Grain, Crude, Trade, Interest, Ship, Wheat and Corn, and we included in our dataset all the non empty documents labeled with at least one of those categories.

in most of the cases this scenario is quite unpractical, if not infeasible.


Interestingly, in the combination of the trigram-class and feedback-loop methods, only 0.1% of all trigram votes are not unanimous.

with the perfect variant, we do include the focus class feature in the copied class label window.


Marcu (1999) uses a decision-tree learner and shallow syntactic features to create classifiers for discourse segmentation and for identifying rhetorical relations.

it is straightforward to determine the labels of all the rhetorical relations from these conditions.


Whether in physical environments (for cars and cell phones) or in virtual ones (for videogames and training simulators), applications are beginning to demand language interfaces that can understand unconstrained speech about constrained domains.

interestingly, while the frame performance declines by 14.3%, the performance on roles only declines 6.4%.


Here the ambiguity is based on the level of description that the speaker intended to convey.

further, the proliferation of both commercial and military applications (e.g., Rickel et al., 2002) involving such virtual worlds suggests that they will continue to become an increasingly important area for natural language research in the future.


Here the leaf nodes represent a basic physical description of the action, while the root nodes represent the highest-level actions for which the leaf actions were performed 2 .

the object filler "DOOR" occurs multiple times.


Any words in the original texts that were not listed in CELEX were discarded, since one of our goals is to compare supervised and unsupervised training.

this research was partially supported by NSF awards IGERt 9870676 and ItR 0085940 and NIMH award 1R0-IMH60922-01A2.


This was 94.9% (bigram) and 94.5% (positional) in German, and 95.2% (bigram) and 93.8% (positional) in English.

we would like to thank Eugene Charniak and our colleagues in BLLIP for their support and helpful suggestions.


We have shown that, despite previous discouraging results (Carroll and Charniak, 1992;Merialdo, 1994), it is possible to achieve good results using EM to learn linguistic structures in an unsupervised way.

in this positional model, each syllable is labeled as initial (i), medial (M), final (F), or as the one syllable in a monosyllabic word (O).


In this section, we describe an algorithm based on two fundamental phonological principles that, when given a set of data from a particular language, will produce a set of rules appropriate to that language.

models with high bias are less sensitive to changes in the observed data.


To formalize the idea of decomposition, we define the factorization of an element u in U as: Each term u i is a factor of u.

bins are constructed based on inflectional families.


Our approach implicitly employs term cooccurrence statistics in the form of statistically derived term clusters.

we performed this procedure on the wall Street Journal (wSJ) portion of the North American News corpus, forming 200 clusters.


At the other extreme, one may submit an entire document for annotation.

looking at the full random set of 1000 sentences, almost all errors (Where * is any entity phrase type, * /O + O/ * errors all errors = 95.43%) are due to problems with phrase boundaries.


(2005) use PropBank subcategorization frames to force a coherent solution.

the type of combination is reported in the last column.


In the latter situation, either a system is a combination of many individual systems (each working with a different parser), or a system extracts features from many different parse trees while exploring the nodes of only one parse tree.

there is a set of non-overlapping arguments labeled with semantic roles associated with each proposition.


These candidate labelings are in turn input to a joint model which can use global features and re-score the candidates.

building such a model is computationally expensive.


The semantic role labeling (SRL) is to assign syntactic constituents with semantic roles (arguments) of predicates (most frequently verbs) in sentences.

we can see that Charniak parser got a better result than Collins parser in the task of SRL.


Note that this strategy is independent of the input syntactic annotation explored, provided it contains clause boundaries.

• Governing category as described in (Gildea and Jurafsky, 2002).


The nodes selected are labeled with B-I-O tags depending if they are at the beginning, inside, or outside of a verb argument.

the labeling architecture is very simple.


From the initial 4,683,777 nodes (of sections 02-21), the heuristic removed 1,503,100 nodes with a loss of 2.6% of the total arguments.

for accomplishing the CoNLL 2005 Shared Task on Semantic Role Labeling (Carreras and Màrquez, 2005), we capitalized on our experience on the semantic shallow parsing by extending our system, widely experimented on PropBank and frameNet (Giuglea and Moschitti, 2004) data, with a twostep boundary detection and a hierarchical argument classification strategy.


One aspect of our work was to use fewer features for the identifier classifier than the basic feature set from (Gildea and Jurafsky, 2002).

an algorithm was defined to combine the results of the argument labeling classifier according to the constraints of the argument labeling problem.


Experiments with the percentage showed that 30% yielded the best F-measure for the identifier classifier.

in the final experimentation, comparison of these two filters showed that the random filter was best for F-measure results of the identifier classifier.


6 http://www.di.unipi.it/˜ruggieri/software.html dual processor server with 2GB memory 7 .

since no embedding is observed for the semantic arguments of predicates, in case of (multiple) embeddings the classifier output was automatically post-processed to retain only the largest embedding constituent.


In each fold, separate SVM classifiers were trained for the Collins and Charniak parses using 75% of the training data.

(2005), we reported on a first attempt to overcome this problem by combining semantic role labels produced from different syntactic parses.


The set of examples produced by this procedure was used to train the final NULL VS NON-NULL classifier.

one system assigned role labels to the nodes in Charniak based trees and a separate system assigned roles to nodes in Collins based trees.


Reranking has previously been applied to semantic role labeling by Toutanova et al.

our different reranking methods vary only in which parse tree is selected; given a parse tree, the frame is always chosen using the best prediction from the base model.


Levenshtein distance is a dynamically computed distance between two strings, accounting for the number of deletions, insertions, and substitutions needed to transform the one string into the other.

we use syntactic trees for deriving instances, partly at the constituent level and partly at the word level.


(2005), the boundary agreement of Charniak is higher than that of Collins; therefore, we choose the Charniak parser's results.

our SRL system is comprised of four stages: pruning, argument classification, classification model incorporation, and integer linear programming.


All experiments were carried out using Section 2 to Section 21 for training, Section 24 for development, and Section 23 for testing.

we have Next, we show how to transform the constraints in 1 http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html the filter function into linear equalities or inequalities, and use them in this ILP.


Although the sec-ondary labels give us much to encourage information, because of data sparseness problem and training efficiency, we stripped off all the secondary labels from the Penn Treebank.


But from the point of view of a SIG on computational natural language learning, this work is probably too much theoryindependent and doesn't teach us enough about language learning.

either to investigate the role of different information sources in learning a task, or to investigate whether the bias of some learning algorithm fits the properties of natural language processing tasks better than alternative learning algorithms, comparative experiments are necessary.


In addition, comparing the results of TOP-Transfer with TOP-Voc-Transfer and TOP-Focus with TOP-Voc-Focus, we can see that adding structural features in TOP-Focus and TOP-Transfer leads to virtually no improvement.

because the original training set already includes the vocabulary from the target domain, the reparameterization approach defined in the preceding section is not necessary so we do not perform it.


More significantly, we use more refined local constraints in the large margin criterion, rather than the global parse-level losses that are commonly considered.

smoothing the link parameters requires us to first extend the notion of word similarity to word-pair similarities, since each link involves two words.


Over the past decade, there has been tremendous progress on learning parsing models from treebank data (Collins, 1997;Charniak, 2000;Wang et al., 2005;McDonald et al., 2005).

second, we show that the large margin training objective can be significantly refined to assess the error of each component of a given parse, rather than just assess a global score.


The average cost of producing a treebank parse can run as high as 30 person-minutes per sentence (20 words on average).

many other natural features, beyond parts-of-speech and abstract grammatical categories, immediately suggest themselves as being predictive of link existence.


To cope with the sparse data problem, we smooth the parameters according to their underlying word similarities by introducing a Laplacian regularizer.

the exact constraint contains a disjunction of two different constraints, which creates a non-convex constraint in w .


Shown are results were only elementary trees with scores higher than 0.3 and 0.1 respectively are used.

dOP, first proposed in (Scha, 1990), can be seen as an early formalization and combination of ideas from construction grammar and statistical parsing.


Thanks to Charles Schafer for sharing his tools on POS/Headword tagging for the Gigaword corpus.

this interplay between the corpus-based and WordNet algorithm works out nicely, resulting in the best model being a combination of all three individual models and achieving a substantially better agreement with both the corpus and human judge than any of the individual models.


Overall, the distributional similarity based method (sim) performs much better than the other methods.

, w n } and target word v the Naïve Bayes probability estimation for the conditional probability of a word v may occur in a given a context C is as follows: where P(w|v) is the probability that a word w appears in the context of a sentence containing v and correspondingly P(w|¬v) is the probability that w appears in a sentence not containing v. The probability estimates were obtained from the processed BNC corpus as follows: |w appears in sentences containing v| |words in sentences containing v| P(w|¬v) = |w occurs in sentences not containing v| |words in sentences not containing v| To avoid 0 probabilities these estimates were smoothed by adding a small constant to all counts and normalizing accordingly.


approach attack false 9 He quickly ran into an unexpected problem.

considering distributional similarity amongst WordNet synonyms seems effective for identifying candidate substitutions that are indeed likely to be applicable in actual texts.


Compared to (Moschitti and Bejan, 2004) where tree fragments are heuristically extracted before applying tree kernels, the MOLT kernel is general and desirable since it does not require such fragment extraction.

3 We denote a marked ordered labeled tree where node n k of an ordered labeled tree U is marked by mark X, n l by Y , and so on, by U @{n k = X, n l = Y, .


More in detail, given training/testing examples where t 1 i and t 2 i are two AST s and v 1 i and v 2 i are two feature vectors associated with two candidate predicate structures s 1 and s 2 , we define the following kernels: where t j i is the j-th AST of the pair e i , K t is the tree kernel function defined in Section 2 and i, j ∈ {1, 2}.

we adopted the Charniak parse trees available at www.lsi.upc.edu/∼srlconll/ along with the official performance evaluator.


(Wu et al., 2004), LN02 (Lee and Ng, 2002) the unseen Senseval-3 test sets.)

fY02 (florian and Yarowsky, 2002) combines classifiers by linear average stacking.


However, with respect to the position-sensitive local context features, these two disambiguation problems may not have much in common since, for instance, we sometimes say "the bank announced", but we rarely say "the save announced".

our methods rival or exceed these stateof-the-art systems on all the data sets.


The probability of a parse tree is computed by summing up the probabilities of all derivations producing it, while the probability of a derivation is computed by multiplying the (smoothed) relative frequencies of its subtrees.

this resulted in a total of 88+ million distinct PCFG rules according to the reduction technique in section 2.


We have a small percentage of positive examples relative to the total number of training examples.

the range of values we select from is: C ∈ 10 −3 , 10 −2 , 10 −1 , 1, 10, 10 2 , 10 3 and γ ∈ 2 −6 , 2 −5 , 2 −4 , ..., 2 4 , 2 6 and for each possible value we perform five-fold cross validation.


using the maximum likelihood estimation for a joint sampling model of observation and label sequences.

word frequencies have been computed to count the number of times that each term (i.e.


Research on the automatic classification of movie or product reviews as positive or negative (e.g., (Pang et al., 2002;Morinaga et al., 2002;Turney and Littman, 2003;Nasukawa and Yi, 2003;Mullen and Collier, 2004;Beineke et al., 2004;Hu and Liu, 2004)) is perhaps the most similar to our work.

sentences that are presenting common background information or introducing an issue and that do not strongly convey any perspective should look similar whether they are in Palestinian or Israeli documents.


The remainder of this paper describes our experiences with a heuristic system for grammar induction.

consider the following equivalence classes, which are proposed relatively early in Directed Alignment: the NT1 of the NT2 in Here, the non-terminals have been assigned on the basis of context-pairs: NT1 is defined by the − of and NT2 is defined by the − in .


For each context-unit token cu in the index, we locate cu in the corpus and create an alignment pattern, such that cu is the left context (C lef t ).


These constraints reduce the number of constituents under consideration at any time to a manageable level.

prepositional-phrases are much less amenable to distributional analysis.


We begin with a discussion of previous distributional approaches to grammar induction in Section 2 and describe their implications in Section 3.

the strictly unsupervised systems of ABL, EMILE and ADIOS have not been evaluated on larger corpora, in part due to efficiency constraints.


The cardinality of this set is at most |P | which is clearly polynomially bounded.


How well, then, could the standard+g model do if it could somehow reduce the number of errors in the top-right cell?

in recent years discriminative probabilistic models have been successfully applied to a number of information extraction tasks in natural language processing (NLP), such as named entity recognition (NER) (McCallum and Li, 2003), noun phrase chunking (Sha and Pereira, 2003) and information extraction from research papers (Peng and McCallum, 2004).


These cover names of people, places and organisations.

in a task such as NER, one sometimes encounters an entity which is difficult to identify using local contextual cues alone because the entity has not be seen before.


As we would expect, the scores are lower than those in Table 5.

as such, the real benefit of including the gazetteer features in standard+g is not fully realised.


One way to handle this problem is to build a language model of content tokens and retain only the maximum likelihood token sequence.

second, no negative information was used during pattern ranking in this experiment.


There are several dimensions along which to classify approaches.

in addition, we aimed at a broad coverage of different language families.


Only a small improvement in labeled attachment score was noticed using the full, nonspecialized classifier to decide the action but discarding its suggestion for label and using a specialized classifier for labeling.

recently statistical dependency parsing techniques have been proposed which are deterministic and/or linear (Yamada and Matsumoto, 2003;Nivre and Scholz, 2004).


In recent years, Perceptron has been used in a number of Natural Language Learning works, such as in Figure 1: Pseudocode of the Perceptron Algorithm.

nevertheless, our error analysis indicates that our model is not rich enough to capture the context required to address complex dependencies.


We saw the same relation in this evaluation: for Turkish, Arabic, and Slovene, languages with limited number of training sentences, our system obtains accuracies below 70%.

as described next, at scoring time y just contains the dependencies found between h and m. We use the cubic-time algorithm for dependency parsing proposed by Eisner (1996;2000).


For example, a semantic role labeling program may start by using a part-of-speech tagger, than apply a shallow parser to chunk the sentence into phrases, and continue by identifying predicates and arguments and then classifying them.

the local search algorithm is presented in Al- We assume a classifier that can give a confidence in its prediction.


The advantage of a pipeline model is that it can use more information that is taken from the outcomes of previous prediction.

our results are compared with the average scores (AV) and the standard deviations (SD), of all the systems participating in the shared task of CoNLL-X.


This motivates the following approach for making the local prediction in a pipeline model more reliable.

for projective languages, we just need 4 actions.


Actually, the head accuracy of "P" is better than the CPOSTAG "C" or "V".

the context features are 2 preceding nodes of node t (and t itself), 2 succeeding nodes of node n (and n itself), and their child nodes.


The three parts of our parser are trained on the available data of the languages.

the POSTAG is also a useful feature for Chinese, and we grouped the original POS tags of Sinica Treebank from 303 to 54 in our preceding work.


Similarly, Japanese label accuracy rose from 93.20% to 93.96%, and labeled attachment accuracy rose from 89.95% to 90.77% when we replaced a maximum entropy classifier with an SVM.

we divided the shared data into training and development test sets, using larger development test sets for the languages supplied with more data.


In principle the vine parser could jointly predict dependency labels along with structures, but parsing runtime would increase by at least a factor of |D|.

we reserved 200 sentences in each language for training the reranker, plus 200 for choosing among rerankers trained on different feature sets and different (U × L)-best lists.


Fixing bound B , no left dependency may exist between child x i and parent x j such that j−i > B (similarly for right dependencies and B r ).

reranking almost always gave some improvement over 1-best parsing.


As with the regular shift-reduce, it uses a stack S and a list of input words W .

1. if arc(T OP, F IRST ) ∈ G, then ra; 2. else if arc(F IRST, T OP ) ∈ G, then la; 3. else if ∃k ∈ Stack, arc(F IRST, k) ∈ G or arc(k, F IRST ) ∈ G, then re; 4. else sh.


Many previous methods are based on history-based models.

one is arc probabilities which are the possibility that two nodes form an arc, and the other is structure probabilities which are used to describe some specific syntactic structures.


That system uses MIRA, an online large-margin learning algorithm, to compute model parameters.

if we only allow projective parses, do not use morphological features and label edges with a simple atomic classifier, the overall drop in performance becomes significant (row 5 versus row 1).


7 The second group mainly contains subjects, objects and different kinds of adjuncts, with a score in the range 60-80% and a distance of 1.8-5.2 IGs to their head.

to analyze in depth the factors determining this variation, and to improve our parsing methods accordingly to meet the challenges posed by the linguistic diversity, will be an important research goal for years to come.


A more fine-grained analysis of the Swedish results reveals a high accuracy for function words, which is compatible with previous studies (Nivre, 2006).

we have the best reported score for Japanese, Swedish and Turkish, and the score for Arabic, Danish, Dutch, Portuguese, Spanish, and overall does not differ significantly from the best one.


The obvious solution is to have a feature that connects the head of a preposition to the child of the preposition.

in the upcoming section, we explain a decoding algorithm for the dependency structures, and later we give a method for learning the weight vector used in the decoding.


Figure 2 illustrates the learning curve of the three languages and Table 3 summarizes the comparisons of "fine vs. coarse" POS types and "forward vs. backward" parsing directions.

the back ward parsing often achieves lower unparsed rate among all languages (except for Japanese and turkish).


We also perform the significant test to evaluate the statistical difference among the three results.

abnormal or wrong push or pop actions usually cause the error propagation to the remaining words in the list.


At the third phase, the post-processor (which is another learner) recognizes the still un-parsed words.

for example, in the Turkish data, 50 % of the categories receive less than 0.1% of the training examples, 2/3 are the right dependency group.


For the other languages, usually the identification rate of the longer distance parsing should be lower than the smaller distance.

this paper presents and analyzes the impact of the efficient parsing model that only combines with lexical and part-of-speech information.


Table 4 gives a set of results for single attributes in Spanish.

the results are typical of the experiments performed with other languages and other attribute combinations: there is a statistically significant improvement going from context=0 to context=1.


Sentence compression holds promise for many applications ranging from summarisation to subtitle generation and subtitle generation.

our assumption is that terms belonging to these chains are indicative of the document's main focus and should therefore be retained in the compressed output.


The resulting document must not only be grammat-ical but also coherent if it is to function as a replacement for the original.

most work to date has focused on a rather simple formulation of sentence compression that does not allow any rewriting operations, besides word removal.


Obtaining an appropriate representation of discourse is the first step towards creating a compression model that exploits contextual information.

we believe this is the first step into understanding how compression can help summarisation.


The FrameNet database lists the surface syntactic realizations of semantic roles, and provides annotated example sentences from the British National Corpus.

an edge cover is a subgraph of a bipartite graph so that each node is linked to at least one node of the other partition.


Our approach is less domain dependent and resource intensive than Narayanan and Harabagiu (2004), it solely employs a dependency parser and the FrameNet database.

we measure the similarity between a question and its candidate answer by matching their predicates and semantic role assignments.


In addition, supervised techniques independently process the answer-bearing sentences, so the answers to the questions may not always be extractable because of algorithmic limitations.

here, top n is the rate at which at least one correct answer is included in the top n answers, while mrr 5 is the average reciprocal rank (1/n) of the highest rank n(n ≤ 5) of a correct answer to each question.


Statistics for these sets are shown in Table 2  To build all alignment systems, we start with 5 iterations of Model 1 followed by 4 iterations of HMM (Vogel et al., 1996), as implemented in GIZA++ (Och and Ney, 2003).

these are presumably close to the alignments that trained bilingual speakers produce.


Our objective here is to demonstrate that this technique works for the widest possible class of models, so we have chosen as the baseline the most widely used phrase-based SMT model.

the features employed are typical of WSD and are therefore far richer than those used in most SMt systems.


Our positive results suggest that our experiments could be tried on other current statistical MT models, especially the growing family of treestructured SMT models employing stochastic transduction grammars of various sorts (Wu and Chiang, 2007).

recently, several researchers have focused on designing WSD systems for the specific purpose of translation.


However, most of previous research has focused on using multilingual resources typically used in SMT systems to improve WSD accuracy, e.g., Dagan and Itai (1994), Li and Li (2002), Diab (2004).

the training set consists of about 1 million sentence pairs in the news domain.


On the one hand, in previous work (Carpuat and Wu, 2005b) we obtained disappointing results when using the predictions of a Senseval WSD system in conjunction with a standard word-based SMT system: we reported slightly lower BLEU scores despite trying to incorporate WSD using a number of apparently sensible methods.

our WSD-based lexical choice models are designed to directly model the lexical choice in the actual translation direction, and take full advantage of not residing strictly within the Bayesian sourcechannel model in order to benefit from the much richer Senseval-style feature set this facilitates.


We were able to obtain ratings on the entire Ziff-Davis test set as it has only 32 instances; this was not possible for Broadcast news as the test section consists of 410 instances.

we chose F1 (as opposed to accuracy or edit distancebased measures) as Clarke and Lapata (2006b) show that it correlates reliably with human judgements.


Higher level parameters will thus naturally be shared by all paragraphs within a single section.

at the leaf level, features may include an analysis of the overlap between the corresponding text and sentence.


Within the latter group, our HIERARCHI-CAL model slightly outperforms the others based on the coarse measure of accuracy at the section level.

in addition to overlap, we use lexical features that capture word co-occurrence patterns in coherent texts.


we divided  3. well-specified otherwise.


For the purpose of cross-validation, the corpus is split into 10 parts.

chodorow and Leacock (2000) use a mutual in-formation measure in addition to raw frequency of ngrams.


the other approach is a novel parser-based method which employs deep linguistic processing to discriminate grammatical input from ungrammatical.


For example, if an auxiliary clustering model q(z | x, y)-perhaps one that did not make such independence assumptions-were used, the posterior probability q(Z i = z | x, y) could be a feature in the proposed model.

conditional distributions over outputs (here, trees) given inputs (here, sentences) have certain advantages.


Terry Koo was funded by a grant from the NSF (DMS-0434222) and a grant from NTT, Agmt.

the techniques above extend easily to the case where dependencies are labeled.


Tokens found in both databases are classified by the post-processing module.

the parser also makes use of suffix analysis.


The additional versions of the parser are inspired by the hypothesis that inclusions make parsing difficult, and this difficulty arises primarily because the parser cannot detect inclusions properly.

overall, the harmonic mean (F) of precision and recall was 65.2 on the random set, 6 points better than 59.2 F observed on the inclusion set.


They present experiments with a dictionarydriven transformation-based learning method and a corpus-based n-gram approach and show that a combination of both methods yields the best results.

it is also important to note that the performance of this model on the inclusion set is still consistently lower than that of all models on the random set.


As an example, consider the second ensemble classifier in Table 7: this classifier excludes the two levels that contain morphological information (morph and all), which represents the most successful individual source of information for our dataset.

work by Hatzivassiloglou and colleagues (Hatzivassiloglou and McKeown, 1993;Hatzivassiloglou and McKeown, 1997;Hatzivassiloglou and wiebe, 2000) used clustering methods to automatically identify adjectival scales from corpora.


Level morph uniformly mapped denominal adjectives to both basic and object (BO).

a set of features is defined for each level of description, and its performance is assessed within the series of experiments.


These results suggest that morphology is the best single source of evidence for our task.

we thus established a different classification for each of the 10 cross-validation runs by assigning each adjective to the class that received most votes.


Because the number of subsets increases exponentially with the number of features, this method is computationally very expensive, and we used a best-first search strategy to alleviate this problem.

denominal and participial adjectives are underrepresented in the Gold Standard, while non-derived and deverbal adjectives are overrepresented.


Since Chinese feature vectors are token-based, we can directly use SoftTFIDF to learn the similarity matrix.

what we need to do is to find a feature model that is robust for all ambiguous personal names.


The reader may be skeptical of our choice to combine a generative PCFG with a discrimative CRF.

recall that α = 0 ignores the morphology model probabilities altogether (using an unweighted lattice), 6 in a correspondence of a morphemes in one string with b in the other, the penalty is a + b − 2, since the morpheme on each side is not in violation.


For instance, since "chair" and "crowd" are distributionally similar to nouns, their presence in the verb clusters can potentially contaminate the clusters with nouns during the bootstrapping process.

our Bengali tagset, which also consists of ten tags, is adapted from the one proposed by Saha et al.


add-on's in existing POS induction algorithms, which remain primarily distributional in nature.

if the new label of a word w differs from its original label, then morphology and context disagree upon the POS of w; and as mentioned above, our method then determines that the word is potentially misclassified.


In this study we assume that the protein names have already been annotated and focus instead on the task of extracting protein-protein interaction sentences for a given protein pair.

the results in table 2 and table 3 are obtained with 10-fold cross-validation.


One of the problems in the field of protein-protein interaction extraction is that different studies generally use different data sets and evaluation metrics.

if a sentence describes an interaction between PROTX1 and PROTX2, it is labeled as positive, otherwise it is labeled as negative.


Sequence alignment is a problem that crops up in many forms, both in computational linguistics (CL) and in other endeavors.

let t be the lowest scoring rhyming word, and let t be the highest-scoring non-rhyming word.


Thus, Klementiev and Roth, in common with the two MT approaches described above, carefully control the features used by the perceptron.

(2004), construct a probabilistic Chinese-English edit model as part of a larger alignment solution, setting edit weights in a heuristic bootstrapped procedure.


The reliance on an explicit alignment and hand-chosen decoys yields a somewhat less flexible solution than that presented here.

the corresponding weight in the "h" interpretation is inhibitory.


The resulting algorithm shows clear promise not only for transcription, but also for ranking of transcriptions and structural classification.

the details of their experiment and model differ from ours in a number of respects.


In addition, our system is also competitive when compared to supervised approaches, being it only 5 points lower than the Class-Example method, while it does not require seed examples and syntactic parsing.

in the literature, the fine-grained classification task has been approached by adopting weakly supervised (Tanev and Magnini, 2006;Fleischman and Hovy, 2002) or unsupervised methods (Cimiano and Völker, 2005).


In all cases we disregard multi-word units that cross the original syntactic bracketing of the WSJ treebank.

there is a sense in which the proposed cure improves on the symptoms, but not the cause of the problem: it weakens independence assumptions by multiplying and hence increasing the specificity of conditioning CFG category labels.


We define the path matching cost function: The denominator is the total number of ordered word pairs in path P .

good phrase pairs may not be selected in the final translation.


As Table 1 makes clear, the EM estimator produces models that are extremely competitive in many-to-1 accuracy and Variation of Information, but are significantly worse in 1-to-1 accuracy.

further, when we examined how the posterior likelihoods varied with increasing iterations of Gibbs sampling, it became apparent that the likelihood was still increasing after 50,000 iterations.


When v 0, f (v) ≈ v − 0.5, so roughly speaking, VB for multinomials involves adding α−0.5 to the expected counts when they are much larger than zero, where α is the Dirichlet prior parameter.

we also showed that EM and other estimators take much longer to converge than usually thought, and often require several hundred iterations to achieve optimal performance.


As Figure 3 shows, the distribution of words to POS tags is highly skewed, with just 6 POS tags, NN, IN, NNP, DT, JJ and NNS, accounting for over 55% of the tokens in the corpus.

in the ad hoc approach the expected count plus α − 1 may be less than zero, resulting in a value of zero for the corresponding parameter (Johnson et al., 2007;.


One of the causes of errors in dependency parsing is the mismatch between analysis results and annotation criteria.

(2) nagai long (bought a long pencil and an eraser) We can find these phrases by referring to the words listed in Table 2-a.


To measure the similarity between potential preand post-conjuncts, a lot of work on the coordination disambiguation used the similarity between conjoined heads.

not only the accuracy of predicate-argument relations ("nB→PB") but also the accuracies of coordinate noun/predicate bunsetsus (related to "nB→nB" and "PB→PB") were improved.


We define this probability as follows: If C i is a nominal clause and consists of a noun n i , we consider the following probability in stead of equation 10: This is because a noun does not have a case frame and any case components in the current framework.

the accuracy of predicate-argument relations ("NB→PB") was improved, but the accuracies of "NB→NB" and "PB→PB" decreased.


Yet, CRF still had the best baseline performance with only local features.

3 Our method described in this section is one of the easiest to implement, while guaranteeing a large margin.


It is still open whether these differences are advantages or disadvantages.

the perceptron algorithm only requires one candidate y ′ for each sequence x i , unlike the training of CRFs where all possible candidates need to be considered.


We would like to investigate various types of new non-local features using the proposed algorithm in future work.

13 First, we obtain we also obtain We used α k (Φ a * − Φ a ) ≤ C a , α k (Φ l * − Φ l ) ≤ C l and C l = C a = C to derive 2C in the second inequality.


The final algorithm resulted in Algorithm 4.2.

by examining the Algorithm 4.2: Perceptron with local and non-local features (parameters: if y 1 ̸ = yi * then (y 1 represents the best in {y n }) proofs in Collins (2002a), we can see that the essential condition for convergence is that the weights are always updated using some y (̸ = y * ) that satisfies: • α ≤ 0 (≤ C in the case of a perceptron with a margin).


Hence, the current study aims at taking an existing Chinese thesaurus, namely the Tongyici Cilin 同義詞詞林, as leverage and extending it with lexical items specific to individual Chinese speech communities.

they were extracted from corpora, and were not limited to nouns.


Results were surprisingly good for target words from the Hong Kong subcorpus.

it is important to see if the classification could accommodate new words from heterogeneous data sources, and whether simple similarity measures and clustering methods could cope with such variation.


The undergraduate students were raised in Hong Kong and the research student in Mainland China.

while human judgement is not straightforward and it is difficult to create a Pan-Chinese lexicon manually, it is observed that combining simple clustering methods with the appropriate data sources appears to be a promising approach toward its automatic construction.


Product features (e.g., "image quality" for digital camera) in a review are good indicators of review quality.

as we will show later in Section 3, these models all suffered from three types of voting bias.


The features on subjectiveness, however, make no contribution.

according to our survey in Section 3, users' votes at Amazon have three kinds of biases as follows: (1) imbalance vote bias, (2) winner circle bias, and (3) early bird bias.


The duplicate sentences will force the translation probability towards the more often seen words.

the optimized model is used to translate the input sentence.


The optimized models also outperform adapted models when the size of the adapted data is small since they make use of all the available data which decrease the influence of data sparseness.

parallel corpus is an indispensable resource for translation model training in statistical machine translation (SMT).


Since large scale monolingual corpora are easier to obtain than parallel corpora.

when the size of corpus achieves a certain scale, the performance may drop.


This cumulative frequency is also divided by the total number of noun hyponyms under that class in WordNet to obtain a smoothed estimate for all nouns under the class.

as well as distributional similarity, researchers have used a variety of statistics as indicators of non-compositionality (Blaheta and Johnson, 2001;Krenn and Evert, 2001).


There is scope for experimenting with other approaches such as (Clark and Weir, 2002), however, we feel a type-based approach is worthwhile to avoid the noise introduced from frequent but polysemous arguments and bias from highly frequent arguments which might be part of a multiword rather than a prototypical argument of the predicate in question, for example eat hat.

two use WordNet and the other uses the entries in a thesaurus of distributionally similar words acquired automatically following (Lin, 1998).


Assuming the avail-ability of a collection of documents and their corresponding manually constructed summaries, these methods attempt to identify the key properties of a good summary, such as the presence of named entities, positional scores, or the location of key phrases.

our first book summarization experiment was done using a re-implementation of an existing state-ofthe-art summarization system.


While there is a significant body of research that has been carried out on the task of text summarization, most of this work has been concerned with the summarization of short documents, with a particular focus on news stories.

on a raw November afternoon, London is enshrouded in heavy fog made harsher by chimney smoke.


For instance, the following is an example drawn from Cliff's Notes summary of Bleak House by Charles Dickens.

a significant number of summarization systems have been proposed during the recent Document Understanding Conference exercises (DUC) -annual evaluations that usually draw the participation of 20-30 teams every year.


The modern Bible is tagged using the C&C maximum entropy tagger (Curran and Clark, 2003), and these tags are transferred from source to target through high-confidence alignments aquired from two alignment approaches.

finally, Wycliffe's Bible was released in the late 14th century, a period when the transition of English from a synthetic to analytical language was finalized.


The purpose of the study was to implement a POS tagger for diachronic texts of maximal accuracy with minimal cost in terms of labor, regardless of the shortcuts taken.

performance drops on the PPCME Test sentences, which come from different domains than the bigram tagger's automatically produced Wycliffe training material.


Of course, our approach can be integrated with one based on annotation by using our bootstrapped taggers to perform semi-automated annotation, even before the first human-annotated tag has been labeled.

with PTB, all noncorresponding PPCME tags were conflated if necessary and mapped to the Penn Treebank tag set.


The effect of the distance measure is less clear-cut, since the Unparsed models perform better with Jaccard, while the Parsed models prefer Cosine.

next, the selectional association of the triple, A(v, r, c), is computed as the ratio of the selectional preference strength for this particular class, divided by the overall selectional preference strength of the verb-relation pair.


Our example in Table 1, which is taken from this dataset, demonstrates its balanced structure: 25 verbs are paired with two arguments and two relations each, such that each argument is highly plausible in one relation, but implausible in the other.

our model can be constructed from a large corpus with partial syntactic information (specifically, subject and object relations) from which it builds an optimally informative vector space.


The underlying assumption is that words with similar meanings occur in similar contexts, and will be assigned similar vectors.


In another solution, assigning each data point to a distinct cluster guarantees perfect homogeneity -each cluster trivially contains only members of a single class.

where C is the target partition, K is the hypothesized partition and h(k) is the size of cluster k. We believe that V-measure provides two significant advantages over Q 0 that make it a more useful diagnostic tool.


using bilingual tuples as basic units ("words"), it is less obvious if perplexity is a useful measure.

new techniques must be deployed to take the best advantage of the limited resources.


We also show that imposing few context-dependent constraints on possible sequences of analyses improves the accuracy of the disambiguation.

selecting an analysis immediately induces a POS tagging for the target word (by projecting the analysis on the POS coordinate).


Figure 1 shows part of a transcribed call.

through experiments with real conversational data, using identified segments and expressions we were able to define appropriate viewpoints and concepts leading to insights for improving the car rental business process.


These include call type classification for the purpose of categorizing calls (Tang et al., 2003) (Zweig et al., 2006), call routing (Kuo and Lee, 2003) (Haffner et al., 2003), obtaining call log summaries (Douglas et al., 2005), agent assisting and monitoring (Mishne et al., 2005), and building of domain models (Roy and Subramaniam, 2006).

to these technologies, in this paper we consider the task of trying to find insights from a collection of complete conversations.


Distinct suffix counts of 2 and above are stored by subtracting this proxy count and converting to the log quantisation scheme described above, i.e., In testing for a suffix count, we first query the item c(w 1 , ..., w n ) = 1 as a proxy for s(w 1 , ..., w n ) = 1 and, if found, query the filter for incrementally larger suffix counts, taking the reconstructed suffix count of an n-gram with a non-zero n-gram count to be the expected value, i.e., Having created a BF containing these events, the algorithm we use to compute the interpolated WB estimate makes use of the inequalities described above to reduce the a priori probability of querying for a negative.

our third set of experiments evaluates the impact of the use of upper bounds between related statistics on translation performance.


Though this is probably the most reliable way, it is impractical for many scenarios since assembling and manually annotating a representative gold standard may already be quite a laborious task.

whereas the gene entities are quite frequent (about 1.3 per sentence on average), the variation entities are rather sparse (0.62 per sentence on average) making it an ideal playground for AL-based annotation.


(1997) use WordNet, looking for a synonymy or hypernymy relation (additionally, for coordinate sisters in Word-Net).

7 they evaluate only on a small subset of discourse-old definite descriptions (those where a wordnet-compatible semantic relation was identified and which were reasonably close to their antecedent), and they did not distinguish coreferent from associative bridging antecedents.


This material is partly based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No.

the free parameters are tuned to directly optimize the evaluation criterion.


we will show that the minimum Bayes risk decision rule results in better translation quality than the maximum aposteriori decision rule for several training criteria.


On the other hand, the computation of the MBR hypotheses is more time consuming.

whether this pays off in terms of translation quality is left open for future work.


The MBR hypotheses were generated using the algorithm described in (Ehling et al., 2007) on a 10 000-best list.

(Och and Ney, 2002;Och, 2003) defined the translation candidate with the minimum word-error rate as pseudo reference translation.


We perform 5-fold cross validation on the training data set to tune parameters and to pick the best model.

in the literature, much of the work on coreference resolution is for English text (Soon et al., 2001;Ng and Cardie, 2002b;Yang et al., 2003;McCallum and Wellner, 2005).


To make the plot more informative, the sentences are ordered by increasing difference.


Recall that each word-pair originates from a dependency path.

figure 1 illustrates various dependency structures of the sentence, rebels fired rockets at a military convoy, after each word is lemmatized.


We let ZKL use the Skew divergence value for these terms: (1−α)pi = p i log 1 1−α .

likewise, the only possible steps permitted in either variant from "wizard#n" and "wizard#a" are to the Synsets that can be expressed with those nodes: "ace#n#3," "sorcerer#n#1," and "charming#a#1."


As an example, one of the best performing is the measure proposed by Jiang and Conrath (1997) (similar to the one proposed by (Lin, 1991)), which finds the shortest path in the taxonomic hierarchy between two candidate words before computing similarity as a function of the information content of the two words and their lowest common subsumer in the hierarchy.

these edges are uni-directional; no edges are created from a Synset to a tokenPOS that can represent the Synset.


In Section 3.1, we explain PARENT METHOD and ANCESTOR METHOD, which determine the modifiee of each chunk based on the likeliness of only one type of the relation.

the cosine distance is equivalent to the Euclidean distance under the condition that the absolute value of every component of string[k] is 1.


A number of conventional models use the pair of 3(pizza-and) and 5(ate) as a negative instance because 3 does not modify 5.

we proposed a novel method for Japanese dependency analysis, which determines the modifiee of each chunk based on the likeliness not only of the parent-child relation but also of the ancestordescendant relation in a dependency tree.


From the above description and Figure 3, the number of training instances used in learning MODEL PARENT is the same as the number of training instances used in learning MODEL ANCESTOR.

when the modifiee is determined using the ancestor-descendant relation, it is necessary to take into account the relations with every node in the tree.


In exploiting ancestor-descendant information, it came to us that redundant information is effectively utilized in a coding problem in communications (Mackay, 2003).

hence, when the parent node of a certain node is being determined, it is required that the parent node should already be included in the tree being constructed.


Our learning method is an extension of Collins's perceptron-based method for sequence labeling (Collins, 2002).

each of the edit operations has an associated cost, and the cost of an alignment is defined as the total cost of edit operations involved in the alignment.


Approaches applied to the problem of empty node recovery fall into three categories.

table 2 displays how well the algorithms trained on WSJ perform on BN.


The BN corpus uses a modified version of annotation guidelines, with some of the modifications affecting gap placement.

we believe that the dependency metric is more appropriate for evaluation on automatically parsed trees because it enforces the most important aspects of tree structure for evaluating gap insertion.


We would like to thank the anomymous reviewers for many helpful comments.

we showed that PAR-SEVAL cannot be used to compare the output of PCFG parsers trained on different treebank annotation schemes, because the results correlate with the ratio of non-terminal/terminal nodes.


In general, declarative sentences have a logical form of type t. Question sentences generally have functional types.

this approach has been integrated with a speech recognizer and shown to be robust to recognition errors (He and Young, 2006).


Interestingly, the optimal α B appears to be superlinear but subquadratic   in K. We used these values of α B in the following experiments.

the modeling assumptions imposed by this alternative are unappealing as they assume the left child and right child are independent given the parent, which is certainly not the case in natural language.


The parameter of the Gaussian, σ 2 , was tuned using the development set.

we used the second noun phrase when the first noun phrase ended with "one", "kind", "sort", or "type", or it ended with "name" followed by "of".


Additionally, we extract all the surface forms used at least in two articles to refer to a Wikipedia entity page.

while Nominator made heavy use of heuristics and lexical clues to solve the structural ambiguity of entity mentions, we employ statistics extracted from Wikipedia and Web search results.


In this section, we describe a method to automatically identify semantically appropriate extraction patterns for use with the sentence classifier.

we then ranked the remaining patterns based on semantic affinity, and evaluated the performance of the top 50, 100, 150, and 200 patterns.


Third, if the IE patterns are restricted to areas of text that are known to be relevant, then it may suffice to use relatively general patterns, which may be easier to learn or acquire.

in some cases, all of the matched words are extracted (e.g., "<# birds>").


In the terrorism domain, we show patterns that extract weapons and perpetrator organizations (PerpOrg).

next, we learn "semantically appropriate" extraction pat-terns by evaluating candidate patterns using a semantic affinity metric.


Many researches have been done in relation extraction.

this proves the great potential inherent in the parse tree structure for relation extraction and our tree kernel takes a big stride towards the right direction.


This is contrast to the tree kernel proposed in Culota and Sorensen (2004) which is context-sensitive, that is, it considers the path from the tree root node to the sub-tree root node.

this suggests the usefulness of extending the tree span beyond SPt for the "predicate-linked" tree span category.


A relation instance between two entities is encaps ulated by a parse tree.

here, λ (0< λ <1) is the decay factor in order to make the kernel value less variable with respect to different sizes of the context-sensitive sub-trees.


One problem with the context-sensitive tree span explored in Zhang et al (2006) is that it only considers the availability of entities' siblings and fails to consider following two factors: 1) Whether is the information contained in SPT enough to determine the relationship between two entities?

it is critical to understand which portion of a parse tree is important in the tree kernel calculation.


Such distortions in the word reordering will be quite difficult for the word or phrase-based alignment model to capture.

conveniently, the chinese Treebank uses the part of speech (POS) tag NT for temporal nouns.


In the following, we discuss each of the three main categories in more detail.

the Chinese possessive structure "NP 1 DEG NP 2 " can express more sophisticated relationships which are inappropriate for the "NP 1 's NP 2 " expression.


Grammar binarization usually maintains an equivalence to the original grammar such that binarized grammars generate the same lan-guage and assign the same probability to each string as the original grammar does.

often no substructures corresponding to partial PTB constituents are extracted to form translation rules.


The last line of Table 2 shows the subset of phrase pairs that contain source phrases found in our development corpus.

a superset of the parallel data was word aligned by GIZa union (Och and Ney, 2003) and EMD (Fraser and Marcu, 2006).


We have shown that it is possible to gain insights from the strengths of the phrase-based extraction model to increase both the phrasal coverage and translation accuracy of the syntax-based model.

additionally, we do a careful study of several syntax-based extraction techniques, testing whether (and how much) they affect phrase pair coverage, and whether (and how much) they affect end-to-end MT accuracy.


The Alignment Template system (ATS) described by Och and Ney (2004) is representative of statistical phrase-based models.

(2004) attach the unaligned source word to the highest possible location, in our example, the S. it is extracted along with our large rule 6, changing the target language sequence to "ó ó ó x 0 x 1 x 2 x 3 x 4 ".


One of the benefits is that we avoid the rather expensive cost of merging the k-best list especially when handling millions of features.

extracting bigram word pair features following the target side ordering implies that the corresponding source side is reordered according to the target side.


We assume that the word pairs follow the target side ordering.


Word pair features reflect the word correspondence in a hierarchical phrase.

this would lead to complex decoding in which all the translated words are memorized for each hypothesis, and thus not integrated in our feature set.


We choose a Chinese corpus as the primary document collection because Chinese text (as well as other Asian languages) has a very large term set and a satisfying subset is usually not smaller than 50000 (Li et al., 2006); on the contrary, a dimensionality lower than 10000 suffices a general English text categorization (Yang and Pedersen, 1997;Rogati and Yang, 2002).

relatively, probability ratio (Forman, 2003) is a more straight metric of discriminability.


Note that, unlike several previous studies, we did not employ additional information from external resources such as gazetteers.

the intuitive effect of maximizing Equation (4) is similar to performing 'softclustering'.


In fact, if we only use a combination of discriminative models (CRFs), which is equivalent to γ j = 0 for all j, we obtain essentially the same objective function as that of the LOP-CRFs.

in this paper, we select one of the simplest; we divide the labeled training data D l into two distinct sets D l and D l .


Second, SS-CRF-MER has a sensitive hyper-parameter in the objective function, which controls the influence of the unlabeled data.

moreover, the division of the labeled training data and the feature set of the generative models were derived in the same manner as our NER experiments (see Section 4.1).


The contributions provided by incorporating unlabeled data in our hybrid model can be seen by comparison with the performance of the first and third rows in HySOL, namely a 2.64 point F-score and a 2.96 point sentence accuracy gain in the NER experiments and a 0.46 point F-score and a 1.99 point sentence accuracy gain in the Chunking experiments.

we divided the labeled training data into 4/5 for estimating Λ and 1/5 for estimating Γ; one feature type shown in    We evaluated the performance in terms of the F β=1 score, which is the evaluation measure used in CoNLL-2000 and, and sentence accuracy, since all the methods in our experiments optimize sequence loss.


Approach to Semi-Supervised SOL In this section, we describe our formulation of a hybrid approach to SOL and a parameter estimation method for sequence predictors.

only a forward-backward algorithm is required for the efficient calculation of our parameter estimation process.


Let N R (x) represent the denominator of Equation (2), that is the normalization factor of R. We can rearrange Equation (2) as follows: where V D i,s represents the potential function of the s-th position of the sequence in the i-th CRF and V G j,s represents the probability of the s-th position in the j-th HMM, that is, xs , respectively.

to these previous studies, this paper proposes a semi-supervised SOL framework based on a hybrid generative and discriminative approach.


In Sequential Viterbi Models, such as HMMs, MEMMs, and Linear Chain CRFs, the type of patterns over output sequences that can be learned by the model depend directly on the model's structure: any pattern that spans more output tags than are covered by the models' order will be very difficult to learn.

for each arc S → Q[x : α] , and each y = x, add a new arc S → Q[y : α] with a weight one.


Moving to higher order model structures increases the distance over which the model can check consistency.

when the model is trained, features will be included for both the subdivided tag and the original (undivided) tag.


If a user is searching for a particular webpage, excluding such results could be harmful.

even more strikingly, adding the Basic count information for the Context tokens (Section 3.2.2) boosts performance by another 8.5%, increasing Qry-Acc by over 22%.


For WindowDiff, lower scores are better.

manually annotated data is rarely available.


The work was supported in part by an ISI seed funding, and in part by a grant from the National Library of Medicine (RO1 LM07061).

the techniques applied rely strongly on the analysis of document structure derived from the webpage's html tags (e.g., the DOM tree model).


(Wick et al., 2006) report extracting database records by learning record field compatibility.

this matches our intuition: without generalizing to semantic language models, higher order language models will be relatively sparse and contain much noise.


Blunsom & Cohn (2006) use Viterbi decoding to find an alignment of two sentences given a trained CRF model, a * argmax a P Λ (a|C i , C j ).

out of the models they describe, the HMM models are the most expressive models that can compute posterior probabilities using the forward-backward algorithm.


Our experiments were limited by the size of the labeled data.

given a source citance C i of length n i , and a target citance C j of length n j , the many-to-one alignment of C i to C j is the relation ;.


"Since activated phosphorylated Chk2 -T68 is involved in phosphorylation and degradation of Cdc25A (Falck et al., 2001, Falck et al., 2002Bartek and Lukas, 2003), we also examined the levels of Cdc25A in 2fTGH and U3A cells exposed to γ-IR."

only metric-based utility functions will result in a metric-based U AM A utility function.


It is easy to see that 1 − U AM A satisfies all the requirements of a metric, i.e., it is non-negative, equals zero if and only if ∼ r =∼ p , symmetric, and obeys the triangle inequality, since if the triangle inequality holds for 1 − U set agreement , it must hold for a sum of 1 − U set agreement values.

unlike sequence alignments, there are no ordering constraints in word alignments, and the alignments are many-to-many as opposed to one-to-one.


Unknown words are a well-known hindrance to natural language applications.

in this work, we are concerned with one concrete problem a machine translation system must face: the one of translating unknown words.


Interestingly, in the hand-aligned case, the vowels absorb most of the complexity since many consonantal cues are heavily evidenced on adjacent vowels.

we wish to induce an HMM over a set of states S for which we also have a function π : S → Y that maps every state in S to a phone in Y .


As such, we also implemented the exact criterion, that is, for each split, we formed a new HMM with s 1 and s 2 merged and calculated the total data likelihood.

we fall short of the best current systems.